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    <title>Search Algorithms: SvarFCI</title>
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            <h2><font color="#ffffff">Search Algorithms: svarGFCI</font></h2>
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<p>
    The svarGFCI algorithm is a version of GFCI for time series data. See the GFCI documentation for

    a description of the GFCI algorithm, which allows for unmeasured (hidden, latent) variables in

    the data-generating process and produces a PAG (partial ancestral graph).</p>
<p>

    svarGFCI takes as input a <em>time lag data set</em>, i.e., a data set which includes time series

    observations of variables X1, X2, X3, ..., and their lags X1:1, X2:1, X3:1, ..., X1:2, X2:2,

    X3:2, ... and so on. X1:n is the nth-lag of the variable X1. To create a time lag data set from a

    standard tabular data set (i.e., a matrix of observations of X1, X2, X3, ...), use the ?create time

    lag data? function in the data manipulation toolbox. The user will be prompted to specify the

    number of lags (n), and a new data set will be created with the above naming convention. The

    new sample size will be the old sample size minus n.
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<p>

    svarGFCI uses a BIC score to search for a skeleton. Thus, the only user-specified parameter is an

    optional <em>penalty score</em> to bias the search in favor of more sparse models. See the description of

    the GES algorithm for discussion of the penalty score. For the traditional definition of the BIC

    score, set the penalty to 1.0. The orientation rules are the same as for FCI.
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<p>

    As is the case with SvarFCI, SvarGFCI will automatically respect the time order of the variables and

    impose a repeating structure. Firstly, it puts lagged variables in appropriate tiers so, e.g., X3:2

    can cause X3:1 and X3 but X3:1 cannot cause X3:2 and X3 cannot cause either X3:1 or X3:2.

    Also, it will assume that the causal structure is the same across time, so that if the edge between

    X1 and X2 is removed because this increases the BIC score, then also the edge between X1:1

    and X2:1 is removed, and so on for additional lags if they exist. When some edge is removed as

    the result of a score increase, all similar (or ?homologous?) edges are also removed.
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<p>
    References:
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<ol>
    <li>Entner, D., & Hoyer, P. O. (2010). On causal discovery from time series data using FCI.
    <li>Proceedings of the Fifth European Workshop on Probabilistic Graphical Models, 121-128.
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